qcnn

NTU Quantum Research Group - Quantum CNN Demonstration with the Uses of MIT TorchQuantum

Quantum-enhanced SAM for skin lesion segmentation on ISIC 2018. Hybrid SAM + Quantum Channel Attention achieves 91.23% IoU.

Quantum Convolution Neural Network

Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a Gradio-based prediction interface.

Quantum Information Processing (QIP) Journal 2026 - Official implementation of the paper "QCNN for Urban Informal Settlement Detection".

Hybrid Quantum–Classical model for brain tumor classification using Quantum FiLM modulation and ResNet-18. Supports multi-class MRI tumor detection with quantum circuit integration.

Quantum Filter Zoo: quantum filter layers (quanvolution, QPF, PQC conv) for PyTorch, built on PennyLane

Hybrid quantum–classical convolutional neural network (QCNN) for MedMNIST classification, with a modular PyTorch–Qiskit pipeline and reproducible experiments.

🧠 Classify brain tumors using a hybrid QCNN with ResNet for accurate MRI image analysis across multiple categories, including no tumor detection.

8-qubit Quantum Convolutional Neural Network (QCNN) for binary skin lesion classification on DermaMNIST, implemented in PennyLane with autoencoder-based dimensionality reduction, Angle-compact quantum embedding, and SU(4) convolutional ansatz. Achieves 60% test accuracy with only 51 trainable parameters.#qml #qcnn #pennylane

Quantum Machine Learning (QML) project that predicts suitable crops based on soil and environmental parameters using quantum-enhanced models. Built as a hybrid application combining classical preprocessing with quantum circuits (via Qiskit/PennyLane), this app demonstrates how quantum computing can be applied to real-world agricultural challenges.

QML Benchmarks is a research-driven repository implementing and benchmarking fundamental quantum algorithms and quantum machine learning models including QCNN, QFT, Grover, Shor, HHL, VQE, and QAOA. The project analyzes algorithm scalability, optimization behavior, and robustness under realistic NISQ noise simulations through structured experiments

A set of quantum machine learning notebooks covering quantum kernels, QNN architecture, and quantum convolutional neural networks.

Quantum-Hybrid Convolutional Neural Network with data re-uploading

Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant.